Complexity and information propagation in hydrological time series of mountain forest catchments

Abstract

Ecosystem analysis is typically done by determination of species composition, structural exploration, determination of matter and energy fluxes and/or system analyses based on deterministic or probabilistic/stochastic model approaches. However, regarding ecosystem dynamics, temporal structure, information content, complexity of signals, and their modifications when subsequently passing through different subsystems, have not intensively been studied to date. Structure in time series characterised by information and complexity measures may provide additional, powerful tools to analyse state and dynamics of ecosystems. Along their path through ecosystem compartments, e.g., hydrological signals are transformed in several ways, comprising changes in randomness, autocorrelation structures, and smoothness. Thus, time series analyses with complexity and information measures are of interest for a holistic understanding of ecosystem behaviour and early indications of natural and anthropogenic disturbances of ecosystems such as ecosystem degradation and climate change. Further, these measures provide additional criteria for the calibration of model parameters, tests of model validity, and determination of the necessary degree of complexity of process models. In this paper, we present the outcome from applications of information and complexity measures to hydrological time series in two climatically different forest ecosystems in South Germany and southern Ecuador. Information and complexity measures are different for different parameters but ecosystems of the same type such as mountain forests exhibit similar behaviour. We hypothesise that complexity of hydraulic time series increases with the number of abiotic and biotic variables involved in the generating process of the time series. Thus, complexity should reach a minimum in the precipitation signal which is controlled by abiotic, atmospheric factors only, and reach a maximum in the root zone where the interaction of abiotic and biotic variables is high. Hydrological time series under study cover the sequence of hydrological signals from open precipitation, throughfall, sapflow, water fluxes in the soil compartment and system discharge. We detected pronounced data aggregation and transformation effects of hydrological signals along their path through subsystems in terms of information propagation. We further found similar patterns in different ecosystems of the same general type. As a result of intensive abiotic and biotic interactions, a pronounced maximum of complexity was found in the moisture signal of the soil compartment.